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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Responsible AI	15%	- Safety and compliance 1. Constitutional AI principles 2. Refusal handling and risk mitigation - Context window optimization 1. Token management and truncation strategies 2. Context retention and summarization
Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. JSON schema enforcement 2. Output validation and reliability
Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Agent loops and control flow 2. Hub-and-spoke multi-agent systems - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling
Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. CLAUDE.md and rules system 2. Custom commands and workflows - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Tool descriptions and selection logic 2. Error handling and validation - Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration

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Anthropic Claude Certified Architect Foundations (CCA-F) 認定 CCA-F 試験問題 (Q69-Q74):

質問 # 69
When configuring Claude Code for a new project, you want to establish team-wide TypeScript strict mode conventions, while keeping your personal Vim keybindings private. What is the correct architectural approach?

A. Option B
B. Option C
C. Option D
D. Option A

正解：A

解説：
Claude Code utilizes a configuration hierarchy. Team-wide standards that should be shared via version control belong in the project- level .claude/CLAUDE.md file. User-level configuration (-/.claude/CLAUDE.md) is for personal preferences and is not shared with the team.

質問 # 70
To extract metadata from different types of reports, a system uses an enum field for document _ type. New, unexpected report types are frequently added over time. What is the recommended schema design pattern to handle this ambiguity?

A. Implement a PostToolUse hook to automatically drop unknown document types.
B. Define a rigid enum with only currently known types and rely on the model to find the closest match.
C. Use an open-ended string field instead of an enum to maximize flexibility.
D. Include an other enum value paired with a string field for extensible categorization.

正解：D

解説：
To handle ambiguous or unexpected data during structured extraction, schemas should include an 'other' (or 'unclear') enum value paired with a detail string field. This prevents the model from forcing unexpected data into incorrect predefined categories.

質問 # 71
Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

A. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
Message may have been sent. Avoid retry."
B. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
Please retry the notification."
C. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.
D. Return is_error: true with the original message content echoed back.

正解：C

解説：
Adding a structured retry_safe flag distinguishes uncertain, potentially idempotent errors from permanent failures. This allows the agent to make informed decisions about automatic retries, preventing duplicate notifications while maintaining reliability for recoverable errors.

質問 # 72
To systematically analyze why developers dismiss certain automated code review findings in a quicktechie.com application, which architectural pattern should be implemented?

A. Run a separate classifier model to predict which findings will be ignored.
B. Add a detected_pattern field to the structured findings to track and aggregate dismissal reasons.
C. Use the Message Batches API to delay findings until end-of-week review.
D. Execute a multi-pass review on every pull request to ensure consensus.

正解：B

解説：
Feedback loop design requires tracking which code constructs trigger specific findings. By including a 'detected_pattern' field in the extraction or review output, teams can systematically analyze pattern clusters when developers dismiss findings, identifying systematic false positives.

質問 # 73
When implementing a self-evaluation loop for structured data extraction, your agent reports 96% aggregate accuracy, but downstream users complain about frequent errors on complex contracts. What monitoring methodology resolves this evaluation visibility gap?

A. Switching entirely to the Message Batches API for asynchronous processing.
B. Tracking accuracy by specific document type and field segment (stratified metrics).
C. Executing the evaluation passes inside a PreToolUse hook.
D. Increasing the overall sampling size randomly to capture a broader dataset.

正解：B

解説：
Aggregate accuracy metrics can mask severe per-document-type failures (e.g., contracts failing frequently while simple receipts succeed nearly 100% of the time). Tracking accuracy using stratified metrics (per document type and field) reveals these hidden failures before automating high-confidence extractions.

質問 # 74
......

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